Research Article Open Access

Extended Trigger Terms for Extracting Adverse Drug Reactions in Social Media Texts

Rami Naim Mohammad Yousef1, Sabrina Tiun2 and Nazlia Omar2
  • 1 Palestine Technical University, Palestinian Authority
  • 2 Universiti Kebangsaan Malaysia, Malaysia
Journal of Computer Science
Volume 15 No. 6, 2019, 873-879

DOI: https://doi.org/10.3844/jcssp.2019.873.879

Submitted On: 6 May 2019 Published On: 29 June 2019

How to Cite: Mohammad Yousef, R. N., Tiun, S. & Omar, N. (2019). Extended Trigger Terms for Extracting Adverse Drug Reactions in Social Media Texts. Journal of Computer Science, 15(6), 873-879. https://doi.org/10.3844/jcssp.2019.873.879

Abstract

Adverse Drug Reaction (ADR) is a disorder caused by taking medications. Studies have addressed extracting ADRs from social networks where users express their opinion regarding a specific medication. Extracting entities mainly depends on specific terms called trigger terms that may occur before or after ADRs. However, these terms should be extended, especially when examining multiple representation of N-gram. This study aims to propose an extension of trigger terms based on the multiple representation of N-gram. Two benchmark datasets are used in the experiments and three classifiers, namely, support vector machine, Naïve Bayes and linear regression, are trained on the proposed extension. Furthermore, two document representations have been utilized including Term Frequency Inverse Document Frequency (TFIDF) and Count Vector (CV). Results show that the proposed extended trigger terms outperform the baseline by achieving 88% and 69% of F1-scores for the first and second datasets, respectively. This finding implies the effectiveness of the proposed extended trigger terms in terms of detecting new ADRs.

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Keywords

  • Adverse Drug Reaction
  • Trigger Terms
  • Feature Extraction
  • Support Vector Machine
  • Linear Regression